We provide for each image a single (image-like) ground truth file encoding semantic-, instance-, and parts- levels annotations. Our compact :doc:`Label format <label_format>` together with
:func:`panoptic_parts.utils.format.decode_uids`
function enable easy decoding of the labels for various image understanding tasks including:
# labels: Python int, or np.ndarray, or tf.Tensor, or torch.tensor
# Semantic Segmentation
semantic_ids, _, _ = decode_uids(labels)
# Instance Segmentation
semantic_ids, instance_ids, _ = decode_uids(labels)
# Panoptic Segmentation
_, _, _, semantic_instance_ids = decode_uids(labels, return_sids_iids=True)
# Parts Segmentation / Parts Parsing
_, _, _, semantic_parts_ids = decode_uids(labels, return_sids_pids=True)
# Instance-level Parts Parsing
semantic_ids, instance_ids, parts_ids = decode_uids(labels)
# Parts-level Panoptic Segmentation
_, _, _, semantic_instance_ids, semantic_parts_ids = decode_uids(labels, return_sids_iids=True, return_sids_pids=True)